Forecasting and stress testing with quantile vector autoregression

Sulkhan Chavleishvili, Simone Manganelli

We introduce a structural quantile vector autoregressive (VAR) model. Unlike standard VAR which models only the average interaction of the endogenous variables, quantile VAR models their interaction at any quantile. We show how to estimate and forecast multivariate quantiles within a recursive structural system. The model is estimated using real and financial variables. The dynamic properties of the system change across quantiles. This is relevant for stress testing exercises, whose goal is to forecast the tail behavior of the economy when hit by large financial and real shocks.

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Year of publication:	[2019]
Authors:	Chavleishvili, Sulkhan ; Manganelli, Simone
Publisher:	Frankfurt am Main, Germany : European Central Bank
Subject:	Regression quantiles \| Structural VAR \| Growth at Risk \| VAR-Modell \| VAR model \| Regressionsanalyse \| Regression analysis \| Prognoseverfahren \| Forecasting model \| Schätzung \| Estimation \| Schock \| Shock \| Risiko \| Risk \| Schätztheorie \| Estimation theory